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In this paper, we develop a Wasserstein autoencoder (WAE) with ahyperspherical prior for multimodal data in the application of inertialconfinement fusion. Unlike a typical hyperspherical generative model thatrequires computationally inefficient sampling from distributions like the vonMis Fisher, we sample from a normal distribution followed by a projection layerbefore the generator. Finally, to determine the validity of the generatedsamples, we exploit a known relationship between the modalities in the datasetas a scientific constraint, and study different properties of the proposedmodel.

Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion